Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations150000
Missing cells0
Missing cells (%)0.0%
Duplicate rows357
Duplicate rows (%)0.2%
Total size in memory12.6 MiB
Average record size in memory88.0 B

Variable types

Categorical1
Numeric10

Alerts

Dataset has 357 (0.2%) duplicate rowsDuplicates
SeriousDlqin2yrs is highly imbalanced (64.6%) Imbalance
RevolvingUtilization is highly skewed (γ1 = 97.63157449) Skewed
Late30_59 is highly skewed (γ1 = 22.59710756) Skewed
DebtRatio is highly skewed (γ1 = 95.15779287) Skewed
MonthlyIncome is highly skewed (γ1 = 127.1216956) Skewed
Late90 is highly skewed (γ1 = 23.08734547) Skewed
Late60_89 is highly skewed (γ1 = 23.33174312) Skewed
RevolvingUtilization has 10878 (7.3%) zeros Zeros
Late30_59 has 126018 (84.0%) zeros Zeros
DebtRatio has 4113 (2.7%) zeros Zeros
MonthlyIncome has 1634 (1.1%) zeros Zeros
OpenCreditLines has 1888 (1.3%) zeros Zeros
Late90 has 141662 (94.4%) zeros Zeros
RealEstateLoans has 56188 (37.5%) zeros Zeros
Late60_89 has 142396 (94.9%) zeros Zeros
Dependents has 90826 (60.6%) zeros Zeros

Reproduction

Analysis started2025-05-30 20:02:51.125462
Analysis finished2025-05-30 20:06:45.263932
Duration3 minutes and 54.14 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

SeriousDlqin2yrs
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
139974 
1
 
10026

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Length

2025-05-31T01:36:46.963826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T01:36:48.841713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 150000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

RevolvingUtilization
Real number (ℝ)

Skewed  Zeros 

Distinct125728
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0484381
Minimum0
Maximum50708
Zeros10878
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:36:50.870586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.029867442
median0.15418074
Q30.55904625
95-th percentile0.9999999
Maximum50708
Range50708
Interquartile range (IQR)0.52917881

Descriptive statistics

Standard deviation249.75537
Coefficient of variation (CV)41.29254
Kurtosis14544.713
Mean6.0484381
Median Absolute Deviation (MAD)0.14832535
Skewness97.631574
Sum907265.71
Variance62377.745
MonotonicityNot monotonic
2025-05-31T01:36:53.430424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10878
 
7.3%
0.9999999 10256
 
6.8%
1 17
 
< 0.1%
0.9500998 8
 
< 0.1%
0.007984032 6
 
< 0.1%
0.954091816 6
 
< 0.1%
0.71314741 6
 
< 0.1%
0.796407186 5
 
< 0.1%
0.988023952 5
 
< 0.1%
0.994011976 5
 
< 0.1%
Other values (125718) 128808
85.9%
ValueCountFrequency (%)
0 10878
7.3%
8.37 × 10-61
 
< 0.1%
9.93 × 10-61
 
< 0.1%
1.25 × 10-51
 
< 0.1%
1.43 × 10-51
 
< 0.1%
1.49 × 10-51
 
< 0.1%
1.51 × 10-51
 
< 0.1%
1.6 × 10-51
 
< 0.1%
1.64 × 10-51
 
< 0.1%
1.87 × 10-51
 
< 0.1%
ValueCountFrequency (%)
50708 1
< 0.1%
29110 1
< 0.1%
22198 1
< 0.1%
22000 1
< 0.1%
20514 1
< 0.1%
18300 1
< 0.1%
17441 1
< 0.1%
13930 1
< 0.1%
13498 1
< 0.1%
13400 1
< 0.1%

Age
Real number (ℝ)

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.295207
Minimum0
Maximum109
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:36:55.811276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q141
median52
Q363
95-th percentile78
Maximum109
Range109
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.771866
Coefficient of variation (CV)0.28247074
Kurtosis-0.49466883
Mean52.295207
Median Absolute Deviation (MAD)11
Skewness0.18899455
Sum7844281
Variance218.20802
MonotonicityNot monotonic
2025-05-31T01:36:58.437115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 3837
 
2.6%
48 3806
 
2.5%
50 3753
 
2.5%
47 3719
 
2.5%
63 3719
 
2.5%
46 3714
 
2.5%
53 3648
 
2.4%
51 3627
 
2.4%
52 3609
 
2.4%
56 3589
 
2.4%
Other values (76) 112979
75.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
21 183
 
0.1%
22 434
 
0.3%
23 641
 
0.4%
24 816
0.5%
25 953
0.6%
26 1193
0.8%
27 1338
0.9%
28 1560
1.0%
29 1702
1.1%
ValueCountFrequency (%)
109 2
 
< 0.1%
107 1
 
< 0.1%
105 1
 
< 0.1%
103 3
 
< 0.1%
102 3
 
< 0.1%
101 3
 
< 0.1%
99 9
< 0.1%
98 6
 
< 0.1%
97 17
< 0.1%
96 18
< 0.1%

Late30_59
Real number (ℝ)

Skewed  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42103333
Minimum0
Maximum98
Zeros126018
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:37:00.676976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1927813
Coefficient of variation (CV)9.9583119
Kurtosis522.37654
Mean0.42103333
Median Absolute Deviation (MAD)0
Skewness22.597108
Sum63155
Variance17.579415
MonotonicityNot monotonic
2025-05-31T01:37:02.750850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 126018
84.0%
1 16033
 
10.7%
2 4598
 
3.1%
3 1754
 
1.2%
4 747
 
0.5%
5 342
 
0.2%
98 264
 
0.2%
6 140
 
0.1%
7 54
 
< 0.1%
8 25
 
< 0.1%
Other values (6) 25
 
< 0.1%
ValueCountFrequency (%)
0 126018
84.0%
1 16033
 
10.7%
2 4598
 
3.1%
3 1754
 
1.2%
4 747
 
0.5%
5 342
 
0.2%
6 140
 
0.1%
7 54
 
< 0.1%
8 25
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
98 264
0.2%
96 5
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 4
 
< 0.1%
9 12
 
< 0.1%
8 25
 
< 0.1%
7 54
 
< 0.1%
6 140
0.1%

DebtRatio
Real number (ℝ)

Skewed  Zeros 

Distinct114194
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.00508
Minimum0
Maximum329664
Zeros4113
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:37:04.872717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004329004
Q10.17507383
median0.36650784
Q30.86825377
95-th percentile2449
Maximum329664
Range329664
Interquartile range (IQR)0.69317994

Descriptive statistics

Standard deviation2037.8185
Coefficient of variation (CV)5.772774
Kurtosis13734.289
Mean353.00508
Median Absolute Deviation (MAD)0.2457228
Skewness95.157793
Sum52950761
Variance4152704.3
MonotonicityNot monotonic
2025-05-31T01:37:07.434554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4113
 
2.7%
1 229
 
0.2%
4 174
 
0.1%
2 170
 
0.1%
3 162
 
0.1%
5 143
 
0.1%
9 125
 
0.1%
10 117
 
0.1%
7 115
 
0.1%
13 114
 
0.1%
Other values (114184) 144538
96.4%
ValueCountFrequency (%)
0 4113
2.7%
2.6 × 10-51
 
< 0.1%
3.69 × 10-51
 
< 0.1%
3.93 × 10-51
 
< 0.1%
6.62 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8 × 10-51
 
< 0.1%
8.57 × 10-51
 
< 0.1%
9.09 × 10-51
 
< 0.1%
9.15 × 10-51
 
< 0.1%
ValueCountFrequency (%)
329664 1
< 0.1%
326442 1
< 0.1%
307001 1
< 0.1%
220516 1
< 0.1%
168835 1
< 0.1%
110952 1
< 0.1%
106885 1
< 0.1%
101320 1
< 0.1%
61907 1
< 0.1%
61106.5 1
< 0.1%

MonthlyIncome
Real number (ℝ)

Skewed  Zeros 

Distinct13594
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6418.4549
Minimum0
Maximum3008750
Zeros1634
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:37:09.991395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1500
Q13903
median5400
Q37400
95-th percentile13500
Maximum3008750
Range3008750
Interquartile range (IQR)3497

Descriptive statistics

Standard deviation12890.396
Coefficient of variation (CV)2.0083331
Kurtosis24261.789
Mean6418.4549
Median Absolute Deviation (MAD)1682
Skewness127.1217
Sum9.6276824 × 108
Variance1.661623 × 108
MonotonicityNot monotonic
2025-05-31T01:37:12.410253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5400 30062
 
20.0%
5000 2757
 
1.8%
4000 2106
 
1.4%
6000 1934
 
1.3%
3000 1758
 
1.2%
0 1634
 
1.1%
2500 1551
 
1.0%
10000 1466
 
1.0%
3500 1360
 
0.9%
4500 1226
 
0.8%
Other values (13584) 104146
69.4%
ValueCountFrequency (%)
0 1634
1.1%
1 605
 
0.4%
2 6
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
3008750 1
< 0.1%
1794060 1
< 0.1%
1560100 1
< 0.1%
1072500 1
< 0.1%
835040 1
< 0.1%
730483 1
< 0.1%
702500 1
< 0.1%
699530 1
< 0.1%
649587 1
< 0.1%
629000 1
< 0.1%

OpenCreditLines
Real number (ℝ)

Zeros 

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.45276
Minimum0
Maximum58
Zeros1888
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:37:14.805103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile18
Maximum58
Range58
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.145951
Coefficient of variation (CV)0.60878944
Kurtosis3.0910667
Mean8.45276
Median Absolute Deviation (MAD)3
Skewness1.2153138
Sum1267914
Variance26.480812
MonotonicityNot monotonic
2025-05-31T01:37:17.093956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 13614
 
9.1%
7 13245
 
8.8%
5 12931
 
8.6%
8 12562
 
8.4%
4 11609
 
7.7%
9 11355
 
7.6%
10 9624
 
6.4%
3 9058
 
6.0%
11 8321
 
5.5%
12 7005
 
4.7%
Other values (48) 40676
27.1%
ValueCountFrequency (%)
0 1888
 
1.3%
1 4438
 
3.0%
2 6666
4.4%
3 9058
6.0%
4 11609
7.7%
5 12931
8.6%
6 13614
9.1%
7 13245
8.8%
8 12562
8.4%
9 11355
7.6%
ValueCountFrequency (%)
58 1
 
< 0.1%
57 2
 
< 0.1%
56 2
 
< 0.1%
54 4
< 0.1%
53 1
 
< 0.1%
52 3
< 0.1%
51 2
 
< 0.1%
50 2
 
< 0.1%
49 4
< 0.1%
48 6
< 0.1%

Late90
Real number (ℝ)

Skewed  Zeros 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26597333
Minimum0
Maximum98
Zeros141662
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:37:19.270822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1693038
Coefficient of variation (CV)15.675646
Kurtosis537.73894
Mean0.26597333
Median Absolute Deviation (MAD)0
Skewness23.087345
Sum39896
Variance17.383094
MonotonicityNot monotonic
2025-05-31T01:37:21.265700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 141662
94.4%
1 5243
 
3.5%
2 1555
 
1.0%
3 667
 
0.4%
4 291
 
0.2%
98 264
 
0.2%
5 131
 
0.1%
6 80
 
0.1%
7 38
 
< 0.1%
8 21
 
< 0.1%
Other values (9) 48
 
< 0.1%
ValueCountFrequency (%)
0 141662
94.4%
1 5243
 
3.5%
2 1555
 
1.0%
3 667
 
0.4%
4 291
 
0.2%
5 131
 
0.1%
6 80
 
0.1%
7 38
 
< 0.1%
8 21
 
< 0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
98 264
0.2%
96 5
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 4
 
< 0.1%
12 2
 
< 0.1%
11 5
 
< 0.1%
10 8
 
< 0.1%
9 19
 
< 0.1%

RealEstateLoans
Real number (ℝ)

Zeros 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.01824
Minimum0
Maximum54
Zeros56188
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:37:23.071588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum54
Range54
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.129771
Coefficient of variation (CV)1.1095331
Kurtosis60.476808
Mean1.01824
Median Absolute Deviation (MAD)1
Skewness3.482484
Sum152736
Variance1.2763825
MonotonicityNot monotonic
2025-05-31T01:37:25.094460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 56188
37.5%
1 52338
34.9%
2 31522
21.0%
3 6300
 
4.2%
4 2170
 
1.4%
5 689
 
0.5%
6 320
 
0.2%
7 171
 
0.1%
8 93
 
0.1%
9 78
 
0.1%
Other values (18) 131
 
0.1%
ValueCountFrequency (%)
0 56188
37.5%
1 52338
34.9%
2 31522
21.0%
3 6300
 
4.2%
4 2170
 
1.4%
5 689
 
0.5%
6 320
 
0.2%
7 171
 
0.1%
8 93
 
0.1%
9 78
 
0.1%
ValueCountFrequency (%)
54 1
 
< 0.1%
32 1
 
< 0.1%
29 1
 
< 0.1%
26 1
 
< 0.1%
25 3
< 0.1%
23 2
< 0.1%
21 1
 
< 0.1%
20 2
< 0.1%
19 2
< 0.1%
18 2
< 0.1%

Late60_89
Real number (ℝ)

Skewed  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24038667
Minimum0
Maximum98
Zeros142396
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:37:26.755360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1551794
Coefficient of variation (CV)17.285399
Kurtosis545.68274
Mean0.24038667
Median Absolute Deviation (MAD)0
Skewness23.331743
Sum36058
Variance17.265516
MonotonicityNot monotonic
2025-05-31T01:37:28.435256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 142396
94.9%
1 5731
 
3.8%
2 1118
 
0.7%
3 318
 
0.2%
98 264
 
0.2%
4 105
 
0.1%
5 34
 
< 0.1%
6 16
 
< 0.1%
7 9
 
< 0.1%
96 5
 
< 0.1%
Other values (3) 4
 
< 0.1%
ValueCountFrequency (%)
0 142396
94.9%
1 5731
 
3.8%
2 1118
 
0.7%
3 318
 
0.2%
4 105
 
0.1%
5 34
 
< 0.1%
6 16
 
< 0.1%
7 9
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
98 264
0.2%
96 5
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 9
 
< 0.1%
6 16
 
< 0.1%
5 34
 
< 0.1%
4 105
 
0.1%
3 318
0.2%

Dependents
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73741333
Minimum0
Maximum20
Zeros90826
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-05-31T01:37:30.172144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1070214
Coefficient of variation (CV)1.5012224
Kurtosis3.1370261
Mean0.73741333
Median Absolute Deviation (MAD)0
Skewness1.6260588
Sum110612
Variance1.2254964
MonotonicityNot monotonic
2025-05-31T01:37:32.232020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 90826
60.6%
1 26316
 
17.5%
2 19522
 
13.0%
3 9483
 
6.3%
4 2862
 
1.9%
5 746
 
0.5%
6 158
 
0.1%
7 51
 
< 0.1%
8 24
 
< 0.1%
10 5
 
< 0.1%
Other values (3) 7
 
< 0.1%
ValueCountFrequency (%)
0 90826
60.6%
1 26316
 
17.5%
2 19522
 
13.0%
3 9483
 
6.3%
4 2862
 
1.9%
5 746
 
0.5%
6 158
 
0.1%
7 51
 
< 0.1%
8 24
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
13 1
 
< 0.1%
10 5
 
< 0.1%
9 5
 
< 0.1%
8 24
 
< 0.1%
7 51
 
< 0.1%
6 158
 
0.1%
5 746
 
0.5%
4 2862
 
1.9%
3 9483
6.3%

Interactions

2025-05-31T01:36:12.524963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:25.179350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:41.576328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:57.454345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:14.292304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:33.579105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:54.460811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:14.556560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:32.905422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:51.703259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:14.984809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:26.989238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:43.285224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:59.184235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:15.886199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:35.294996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:57.299632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:16.195459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:34.651313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:53.711129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:17.114681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:28.379151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:44.795132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:00.784137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:20.511914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:36.983896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:59.242512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:17.637367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:36.257211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:55.508018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:19.104554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:29.759066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:46.204046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:02.352041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:22.188810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:38.673786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:01.357378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:19.143275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:37.816118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:58.787814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:21.314416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:31.311967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:47.947936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:04.236928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:23.980699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:40.516673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:02.993278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:20.848170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:39.675002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:00.777695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:23.993251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:32.995862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:49.563835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:06.025815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:25.581600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:42.848528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:04.798165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:22.566064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:42.091851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:03.007553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:28.055000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:34.766753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:51.187733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:07.892696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:27.134503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:45.266376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:06.298076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:25.755868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:44.362711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:05.010429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:30.254864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:36.355650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:52.679641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:09.681586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:28.646409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:47.503237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:08.152957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:27.452760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:46.297591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:06.642327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:32.301737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:38.036554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:54.144549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:11.154496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:30.260307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:49.929089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:10.014842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:29.186652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:48.045480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:08.512212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:34.454606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:39.737442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:33:55.738452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:12.614405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:31.713218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:34:51.788976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:12.620681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:30.836551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:35:49.723381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-31T01:36:10.163110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-31T01:37:33.844917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeDebtRatioDependentsLate30_59Late60_89Late90MonthlyIncomeOpenCreditLinesRealEstateLoansRevolvingUtilizationSeriousDlqin2yrs
Age1.0000.029-0.232-0.095-0.085-0.1040.1060.1580.054-0.2780.116
DebtRatio0.0291.000-0.0600.0380.001-0.032-0.0940.2270.4000.0770.000
Dependents-0.232-0.0601.0000.0730.0360.0300.1840.1110.1720.1230.032
Late30_59-0.0950.0380.0731.0000.2800.253-0.0140.0640.0220.2340.084
Late60_89-0.0850.0010.0360.2801.0000.321-0.048-0.048-0.0440.1880.082
Late90-0.104-0.0320.0300.2530.3211.000-0.079-0.135-0.1010.2380.085
MonthlyIncome0.106-0.0940.184-0.014-0.048-0.0791.0000.2730.347-0.0650.000
OpenCreditLines0.1580.2270.1110.064-0.048-0.1350.2731.0000.473-0.0870.049
RealEstateLoans0.0540.4000.1720.022-0.044-0.1010.3470.4731.000-0.0270.033
RevolvingUtilization-0.2780.0770.1230.2340.1880.238-0.065-0.087-0.0271.0000.000
SeriousDlqin2yrs0.1160.0000.0320.0840.0820.0850.0000.0490.0330.0001.000

Missing values

2025-05-31T01:36:37.481413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-31T01:36:42.009135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SeriousDlqin2yrsRevolvingUtilizationAgeLate30_59DebtRatioMonthlyIncomeOpenCreditLinesLate90RealEstateLoansLate60_89Dependents
010.7661274520.8029829120.0130602.0
100.9571514000.1218762600.040001.0
200.6581803810.0851133042.021000.0
300.2338103000.0360503300.050000.0
400.9072394910.02492663588.070100.0
500.2131797400.3756073500.030101.0
600.3056825705710.0000005400.080300.0
700.7544643900.2099403500.080000.0
800.11695127046.0000005400.020000.0
900.1891695700.60629123684.090402.0
SeriousDlqin2yrsRevolvingUtilizationAgeLate30_59DebtRatioMonthlyIncomeOpenCreditLinesLate90RealEstateLoansLate60_89Dependents
14999000.0555184600.6097794335.070102.0
14999100.1041125900.47765810316.0100200.0
14999200.8719765004132.0000005400.0110103.0
14999301.0000002200.000000820.010000.0
14999400.3857425000.4042933400.070000.0
14999500.0406747400.2251312100.040100.0
14999600.2997454400.7165625584.040102.0
14999700.2460445803870.0000005400.0180100.0
14999800.0000003000.0000005716.040000.0
14999900.8502836400.2499088158.080200.0

Duplicate rows

Most frequently occurring

SeriousDlqin2yrsRevolvingUtilizationAgeLate30_59DebtRatioMonthlyIncomeOpenCreditLinesLate90RealEstateLoansLate60_89Dependents# duplicates
21001.022980.05400.00980980.015
20201.02200.0820.010000.012
21401.02300.05400.000000.010
500.02200.0820.020000.08
19801.021980.05400.00980980.08
21501.02300.05400.010000.08
25601.03700.05400.000000.08
900.02200.05400.010000.07
1600.02300.05400.010000.07
2300.02500.05400.010000.07